Julia From Julia'S Zero To Hero 2 In 1

What you'll learn
Extract and handle your data with Julia
Uncover the concepts of metaprogramming in Julia
Conduct statistical analysis with StatsBase .jl and Distributions .jl
Build your data science models
Explore big data concepts in Julia
Learn to to write high performance Julia code.
Requirements
This Learning Path is designed specifically for data scientists, data analysts or statisticians but is also suitable for any programmer.
Description
Are you looking forward to get well versed with Julia? Then this is the perfect course for you!Julia is a young language with limited documentation and although rapidly growing, a small user community. Most developers today will know the object oriented paradigm used in mainstream languages such as Python, Java and C++. This presents a challenge switching to Julia which is more functionally oriented.With this comprehensive 2-in-1 course takes a practical and incremental approach. It teaches the fundamentals of Julia to developers with basic knowledge of programming. It is taught in a hands on approach, with simple programming examples the student can try themselves. Building on that, it will invite the user to a tour of the ecosystem of Julia through practical code examples.By end of this course you will more productive and acquire all the skills to work with data more efficiently. Also help you quickly refresh your knowledge of functions, modules, and arrays & shows you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation & also get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Getting Started With Julia covers complete INSTALLATION AND SETUP along with basic of Julia. This course will not only introduce the language, but also explain how to think differently about problems with the Julia approach. This course also focuses various aspects such as Functional Programming in Julia, Metaprogramming, Debugging and Testing & much more.The second course, Julia Solutions covers consist complete guide to programming with Julia for performing numerical computation will make you more productive and able to work with data more efficiently. The course starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We'll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you'll see how to optimize data science programs with parallel computing and memory allocation. You'll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This course also includes videos on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the course, you will acquire the skills to work more effectively with your data.About the Authors:Erik Engheim is a professional mobile developer with experience in many different programming languages, often in combination. Erik Engheim has worked with C/C#, Java, C++, Objective-C, and Swift before moving into Julia. His experience with Julia involves automation, and high performance processing of code strings.Jalem Raj Rohit is an IIT Jodhpur graduate with a keen interest in machine learning, data science, data analysis, computational statistics, and natural language processing (NLP). Rohit currently works as a senior data scientist at Zomato, also having worked as the first data scientist at Kayako.He is part of the Julia project, where he develops data science models and contributes to the codebase. Additionally, Raj is also a Mozilla contributor and volunteer, and he has interned at Scimergent Analytics.
Overview
Section 1: Getting Started With Julia
Lecture 1 The Course Overview
Lecture 2 Downloading Julia
Lecture 3 Setting up an Editor
Lecture 4 Using the Julia REPL
Lecture 5 Numbers
Lecture 6 Strings
Lecture 7 Arrays
Lecture 8 Control Flow
Lecture 9 Functions
Lecture 10 Variables
Lecture 11 Dictionaries
Lecture 12 Practical Usage of Functions
Lecture 13 Inspecting Types
Lecture 14 Type Hierarchies and Multiple Dispatch
Lecture 15 Conversion and Promotion
Lecture 16 Defining Your Own Types
Lecture 17 Reading and Writing to Files
Lecture 18 Networking
Lecture 19 Dealing with Different File Formats
Lecture 20 Using Modules
Lecture 21 Networking
Lecture 22 Reading and Writing CSV Files
Lecture 23 Interfaces
Lecture 24 Maze Builder
Lecture 25 Graphics Editor
Lecture 26 Implementation Inheritance
Lecture 27 Higher Order Functions
Lecture 28 Function Composition
Lecture 29 Functional Approach
Lecture 30 Functional Interpreter Pattern
Lecture 31 Common Traits
Lecture 32 Collection Types
Lecture 33 Multidimensional Arrays
Lecture 34 Sets
Lecture 35 Introducing Type Unions
Lecture 36 Code Reuse Through Type Unions
Lecture 37 Why Parametric Types?
Lecture 38 Creating a Generic Collection
Lecture 39 Pitfalls
Lecture 40 Nullable
Lecture 41 Debugging Approaches
Lecture 42 Writing Debuggable Code
Lecture 43 Writing Tests
Lecture 44 Program Representation
Lecture 45 Macros
Lecture 46 Code Generation
Lecture 47 Compilation
Lecture 48 Abstract Versus Concrete Types
Lecture 49 Type Stability
Section 2: Julia Solutions
Lecture 50 The Course Overview
Lecture 51 Handling Data with CSV Files
Lecture 52 Handling Data with TSV Files
Lecture 53 Interacting with the Web
Lecture 54 Representation of a Julia Program
Lecture 55 Symbols
Lecture 56 Quoting
Lecture 57 Interpolation
Lecture 58 The eval Function
Lecture 59 Macros
Lecture 60 Metaprogramming with DataFrames
Lecture 61 Basic Statistics Concepts
Lecture 62 Descriptive Statistics
Lecture 63 Deviation Metrics
Lecture 64 Sampling
Lecture 65 Correlation Analysis
Lecture 66 Dimensionality Reduction
Lecture 67 Data Preprocessing
Lecture 68 Linear Regression
Lecture 69 Classification
Lecture 70 Performance Evaluation and Model Selection
Lecture 71 Cross Validation
Lecture 72 Distances
Lecture 73 Distributions
Lecture 74 Time Series Analysis
Lecture 75 Plotting Basic Arrays
Lecture 76 Plotting DataFrames
Lecture 77 Plotting Functions
Lecture 78 Exploratory Data Analytics Through Plots
Lecture 79 Line Plots
Lecture 80 Scatter Plots
Lecture 81 Histograms
Lecture 82 Aesthetic Customizations
Lecture 83 Basic Concepts of Parallel Computing
Lecture 84 Data Movement
Lecture 85 Parallel Maps and Loop Operations
Lecture 86 Channels
This Learning Path is designed specifically for data scientists, data analysts or statisticians but is also suitable for any programmer who is new to the field of data science, or anyone aspiring to get into the field of data science and choses Julia as the tool to do so.


Homepage

https://www.udemy.com/course/julia-from-julias-zero-to-hero-2-in-1/